title: Information metrics in genetic epidemiology.
abstract: Information-theoretic metrics have been proposed for studying gene-gene and gene-environment interactions in genetic epidemiology. Although these metrics have proven very promising, they are typically interpreted in the context of communications and information transmission, diminishing their tangibility for epidemiologists and statisticians. In this paper, we clarify the interpretation of information-theoretic metrics. In particular, we develop the methods so that their relation to the global properties of probability models is made clear and contrast them with log-linear models for multinomial data. Hopefully, a better understanding of their properties and probabilistic implications will promote their acceptance and correct usage in genetic epidemiology. Our novel development also suggests new approaches to model search and computation.
authors.affiliation: State University of New York at Buffalo, Buffalo, NY, USA. dtritch@rogers.com
authors.forename: David L
authors.initials: DL
authors.lastname: Tritchler
authors.forename: Lara
authors.initials: L
authors.lastname: Sucheston
authors.forename: Pritam
authors.initials: P
authors.lastname: Chanda
authors.forename: Murali
authors.initials: M
authors.lastname: Ramanathan
mesh_terms: D000465:Algorithms; D001244:Association; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D004777:Environment; D004843:Epistasis, Genetic / Q000235:genetics; D007257:Information Theory; D008957:Models, Genetic; D017720:Molecular Epidemiology / Q000706:statistics & numerical data*; D010641:Phenotype; D011336:Probability